1 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.CL2023★ 1 cited
Instruct and Extract: Instruction Tuning for On-Demand Information Extraction
Yizhu Jiao, Ming Zhong, Sha Li +4
Large language models with instruction-following capabilities open the door to a wider group of users. However, when it comes to information extraction - a classic task in natural…
cs.CL2023★ 1 cited
The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions
Siru Ouyang, Shuohang Wang, Yang Liu +7
Recent progress in Large Language Models (LLMs) has produced models that exhibit remarkable performance across a variety of NLP tasks. However, it remains unclear whether the exist…
cs.CL2023★ 1 cited
Ontology Enrichment for Effective Fine-grained Entity Typing
Siru Ouyang, Jiaxin Huang, Pranav Pillai +3
Fine-grained entity typing (FET) is the task of identifying specific entity types at a fine-grained level for entity mentions based on their contextual information. Conventional me…